This code is for our paper "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers" which is part of the supplementary materials for ICCV 2021 Workshop on Computer Vision for Automated Medical Diagnosis. The paper has since been accpeted and presented at ICCV 2021 Workshop.
https://arxiv.org/abs/2104.06757
https://openaccess.thecvf.com/content/ICCV2021W/CVAMD/html/Kamran_VTGAN_Semi-Supervised_Retinal_Image_Synthesis_and_Disease_Prediction_Using_Vision_ICCVW_2021_paper.html
https://ieeexplore.ieee.org/document/9607858
@INPROCEEDINGS{9607858,
author={Kamran, Sharif Amit and Hossain, Khondker Fariha and Tavakkoli, Alireza and Zuckerbrod, Stewart Lee and Baker, Salah A.},
booktitle={2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
title={VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers},
year={2021},
volume={},
number={},
pages={3228-3238},
doi={10.1109/ICCVW54120.2021.00362}
}
- Ubuntu 18.04 / Windows 7 or later
- NVIDIA Graphics card
- Download and Install Nvidia Drivers
- Download and Install via Runfile Nvidia Cuda Toolkit 11.2
- Download and Install Nvidia CuDNN 8.1.0 or later
- Install Pip3 and Python3 enviornment
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.7
- Install Tensorflow-Gpu version-2.5.0 and Keras version-2.5.0
sudo pip3 install tensorflow-gpu
sudo pip3 install keras
- Install packages from requirements.txt
sudo pip3 install -r requirements.txt
https://sites.google.com/site/hosseinrabbanikhorasgani/datasets-1/fundus-fluorescein-angiogram-photographs--colour-fundus-images-of-diabetic-patients
- Please cite the paper if you use their data
@article{hajeb2012diabetic,
title={Diabetic retinopathy grading by digital curvelet transform},
author={Hajeb Mohammad Alipour, Shirin and Rabbani, Hossein and Akhlaghi, Mohammad Reza},
journal={Computational and mathematical methods in medicine},
volume={2012},
year={2012},
publisher={Hindawi}
}
- Folder structure for data-preprocessing given below. Please make sure it matches with your local repository.
├── Dataset
| ├──ABNORMAL
| ├──NORMAL
- Type this in terminal to run the random_crop.py file
python3 random_crop.py --output_dir=data --input_dim=512 --datadir=Dataset
- There are different flags to choose from. Not all of them are mandatory.
'--input_dim', type=int, default=512
'--n_crops', type=int, default=50
'--datadir', type=str, required=True, help='path/to/data_directory',default='Dataset'
'--output_dir', type=str, default='data'
- Convert all the images to npz format
python3 convert_npz.py --outfile_name=vtgan --input_dim=512 --datadir=data --n_crops=50
- There are different flags to choose from. Not all of them are mandatory.
'--input_dim', type=int, default=512
'--n_crops', type=int, default=50
'--datadir', type=str, required=True, help='path/to/data_directory',default='data'
'--outfile_name', type=str, default='vtgan'
'--n_images', type=int, default=17
- Type this in terminal to run the train.py file
python3 train.py --npz_file=vtgan --batch=2 --epochs=100 --savedir=VTGAN
- There are different flags to choose from. Not all of them are mandatory
'--epochs', type=int, default=100
'--batch_size', type=int, default=2
'--npz_file', type=str, default='vtgan', help='path/to/npz/file'
'--input_dim', type=int, default=512
'--n_patch', type=int, default=64
'--savedir', type=str, required=False, help='path/to/save_directory',default='VTGAN'
'--resume_training', type=str, required=False, default='no', choices=['yes','no']
The code is released under the BSD 3-Clause License, you can read the license file included in the repository for details.
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